Unmanned aerial vehicle system interoperability evaluation method and system based on Kalman filtering

Abstract

The invention discloses an unmanned aerial vehicle system interoperability evaluation method based on Kalman filtering. The method comprises the following steps: constructing a grade model comprising a structure attribute, an application attribute, a facility attribute, a data attribute and a knowledge attribute; constructing second-level index trees based on the attributes, and combining the second-level index trees to obtain an overall attribute index tree; scoring based on the overall attribute index tree to obtain a second-level index score and a weight operator corresponding to each attribute; obtaining a corresponding attribute state equation based on the corresponding secondary index score and the weight operator; fusing based on the structure attribute state equation and the application attribute state equation to obtain a first attribute value; fusing based on the first attribute value and a facility attribute state equation to obtain a second attribute value; circularly executing the fusion process, and sequentially fusing the data attribute state equation and the knowledge attribute state equation to finally obtain a fourth attribute value; and obtaining an interoperability grade based on the fourth attribute value and a grade model. And the evaluation accuracy of the interoperability of the unmanned aerial vehicle system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of interoperability evaluation of unmanned aerial vehicle systems, and more particularly to a method and system for interoperability evaluation of unmanned aerial vehicle systems based on Kalman filtering. Background Art

[0002] UAV systems usually consist of an unmanned aerial vehicle and a ground control station. Compared with manned aircraft, it has two main advantages: one is the elimination of the risk to the pilot's life, and the other is the extraordinary aviation capabilities such as endurance that are not limited by humans. UAVs improve low-altitude detection technology by using a stable design that was originally dangerous to humans, protecting the lives of pilots by performing "boring, dirty or dangerous" tasks, and the procurement and operating costs are lower than manned aircraft. Based on this, the purchase volume of UAVs has shown a significant upward trend in recent years. When performing tasks, UAV systems need to be integrated into their respective operations and need to interoperate with manned aircraft, other UAV systems, communication data link operators, etc. Therefore, it is particularly important to develop UAV systems that can achieve synergy between the above systems and evaluate the interoperability of UAV systems.

[0003] The typical system interoperability measurement model is the LISI model proposed in 1998. The LISI model divides system interoperability into five levels from low to high: isolation level, connection level, function level, domain level and cross-domain level, and identifies the four attributes of procedures (P), applications (A), facilities (I) and data (D) that affect the interoperability of each level. However, its operability evaluation is carried out under the old construction system. The main characteristics of the current UAV system have changed from construction to service-oriented, networked, GIG system, "net cloud", "military cloud brain" and other new systems, which has changed the connotation of the original attributes when evaluating the interoperability of UAV systems. At the same time, given the impact of new technologies such as the Internet of Things and deep learning on UAV systems, the accuracy of UAV system interoperability evaluation no longer meets the requirements of the new system.

[0004] Therefore, how to improve the accuracy of UAV system interoperability assessment results on the basis of meeting the requirements of the new system is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a UAV system interoperability evaluation method and system based on Kalman filtering, which expands the connotation of UAV interoperability under the new system, adds knowledge attributes, expands the key features of each attribute at different levels, and divides the five attributes into secondary indicator trees, thereby improving the accuracy of UAV system interoperability evaluation.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A Kalman filter-based UAV system interoperability assessment method includes:

[0008] Construct a UAV system interoperability level model including structure attributes, application attributes, facility attributes, data attributes and knowledge attributes;

[0009] Based on the above attributes, construct the secondary index trees respectively and merge them to obtain the overall attribute index tree;

[0010] Scoring is performed based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute;

[0011] Obtaining a corresponding attribute state equation based on the corresponding secondary index score and the weight operator;

[0012] A first attribute value is obtained by fusing the structural attribute state equation with the application attribute state equation;

[0013] Based on the fusion of the first attribute value and the facility attribute state equation, a second attribute value is obtained;

[0014] The above fusion process is executed cyclically, and the data attribute state equation and the knowledge attribute state equation are fused in sequence, and finally the fourth attribute value is obtained;

[0015] Based on the fourth attribute value and the UAV system interoperability level model, an interoperability level is obtained.

[0016] Preferably, the UAV system interoperability level model includes:

[0017] The isolation level is 0, the connection level is 1, the functional level is 2, the integration level is 3, the collaboration level is 4, and the general level is 5.

[0018] Preferably, based on the above attributes, secondary index trees are constructed respectively, and correspondingly, a structural attribute index tree, an application attribute index tree, a facility attribute index tree, a data attribute index tree and a knowledge attribute index tree are obtained;

[0019] The above attribute indicator trees all include: target attributes, multiple first-level indicators and multiple second-level indicators;

[0020] Each of the first-level indicators corresponds to a plurality of the second-level indicators.

[0021] Preferably, the first-level indicators of the structural attribute indicator tree include: data structure, system structure and management structure;

[0022] The first-level indicators of the application attribute indicator tree include: interaction model, implementation operation and application scale;

[0023] The first-level indicators of the facility attribute indicator tree include: facility controllability, protocol practicality and facility robustness;

[0024] The first-level indicators of the data attribute indicator tree include: simplex, duplex and half-duplex, and duplex;

[0025] The first-level indicators of the knowledge attribute indicator tree include: transmission information, understanding information and prediction information.

[0026] Preferably, the secondary indicators of the data structure include: basic data standards, data service standards and data management standards;

[0027] The secondary indicators of the system structure include: physical layer, data layer and semantic layer;

[0028] The secondary indicators of the management structure include: standardization, universality and integration of regulations and rules;

[0029] The secondary indicators of the interaction model include: interaction delay, interaction mode and interaction direction;

[0030] The secondary indicators for implementing the operation include: endurance, flight altitude and speed;

[0031] The secondary indicators of the application scale include: the number of systems, the number of software and the number of hardware;

[0032] The secondary indicators of facility controllability include: command controllability, management controllability and collaboration controllability;

[0033] The secondary indicators of the protocol practicality include: protocol architecture, support scale and service type;

[0034] The secondary indicators of facility robustness include: link integrity, information redundancy and anti-destruction capability;

[0035] The secondary indicators of the simplex include: communication confidentiality, communication timeliness and information capacity;

[0036] The secondary indicators of the dual half-work include: instruction correctness, feedback timeliness and data confidentiality;

[0037] The secondary indicators of duplex include: data confidentiality, communication fluency, and information acquisition;

[0038] The secondary indicators of the transmission information include: images, videos and voices;

[0039] The secondary indicators of understanding information include: knowledge network, environment model and target estimation;

[0040] The secondary indicators of the forecast information include: situation forecast, model correction and command decision.

[0041] Preferably, multiple secondary index scores and multiple weight operators corresponding to each attribute are obtained, specifically including:

[0042] Scoring the secondary indicators of all attributes based on the expert experience of relevant professionals, and obtaining multiple secondary indicator scores corresponding to each attribute;

[0043] Based on the expert experience of the relevant professionals, a plurality of first weight operators are provided between the primary index of each attribute and the corresponding attribute, and a plurality of second weight operators are provided between the secondary index of each attribute and the corresponding primary index;

[0044] The weight operator is composed based on the first weight operator and the second weight operator.

[0045] Preferably, the corresponding property state equation is obtained, specifically including:

[0046] Obtaining a corresponding attribute state matrix based on the first weight operator and the second weight operator corresponding to each attribute;

[0047] Based on the attribute state matrix and the corresponding secondary index score, the corresponding attribute state quantity prediction equation X(k) is calculated:

[0048] X(k)=F X X(k-1)X∈{P,A,I,D,K}

[0049] Among them, k represents the state at the next moment, F X represents the X attribute state matrix, X(k-1) represents the vector composed of all secondary indicator scores of X attribute, k-1 represents the current state, P, A, I, D and K represent structural attributes, application attributes, facility attributes, data attributes and knowledge attributes respectively;

[0050] Based on the attribute state quantity prediction equation X(k), the corresponding attribute covariance prediction equation is obtained:

[0051]

[0052] Among them, ∑ X(k-1) represents the covariance matrix of the X attribute at the previous moment, and T represents the transposed symbol;

[0053] The attribute state quantity prediction equation and the attribute covariance prediction equation together constitute the attribute state equation.

[0054] Preferably, the secondary indicator score corresponding to the secondary indicator with the largest second weight operator among the primary indicators of each attribute participates in the calculation of the attribute state equation.

[0055] Preferably, the fusion process is:

[0056] The Kalman gain M is calculated based on the property state equation k :

[0057]

[0058] Among them, F Y represents the Y attribute state matrix, and R represents the comprehensive noise;

[0059] The nth attribute value P(k+n) is calculated based on the Kalman gain:

[0060] P(k+n)=P(k)+M k (Y(1)-F Y P(k)

[0061] Based on the nth attribute value, the attribute value covariance matrix ∑ P(k+n) :

[0062] ∑ P(k+n) =(1-M k F Y )∑ P(k)

[0063] Where n represents the number of fusions.

[0064] A Kalman filter-based UAV system interoperability evaluation system comprises: a hierarchical model building module, an indicator tree building module, a scoring module, a state equation building module, a loop fusion module and a result output module;

[0065] The hierarchical model building module is used to build a UAV system interoperability hierarchical model including structural attributes, application attributes, facility attributes, data attributes and knowledge attributes;

[0066] The indicator tree construction module is used to construct secondary indicator trees based on the above attributes and merge them to obtain an overall attribute indicator tree;

[0067] The scoring module is used to score based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute;

[0068] The state equation construction module is used to obtain the corresponding attribute state equation based on the corresponding secondary index score and the weight operator;

[0069] The loop fusion module is used to fuse the structure attribute state equation with the application attribute state equation to obtain a first attribute value; fuse the first attribute value with the facility attribute state equation to obtain a second attribute value; and loop through the above fusion process to fuse the data attribute state equation and the knowledge attribute state equation in turn to finally obtain a fourth attribute value;

[0070] The result output module is used to obtain the interoperability level based on the fourth attribute value and the UAV system interoperability level model.

[0071] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a method and system for evaluating the interoperability of unmanned aerial vehicle systems based on Kalman filtering, which expands the connotation of unmanned aerial vehicle interoperability under a new system, adds knowledge attributes, expands the key features of each attribute at different levels, and divides the five attributes into secondary indicator trees respectively, and combines qualitative evaluation with quantitative evaluation, thereby improving the accuracy of the evaluation results of the interoperability of unmanned aerial vehicle systems.

[0072] By constructing the secondary evaluation indicators under the interoperability evaluation attributes of the UAV system, the interoperability attribute scores were specifically quantified; an interoperability evaluation method based on Kalman filtering was proposed, and the interoperability level was obtained from the quantified values ​​of the secondary indicators, and an interoperability evaluation system was established. The developed interoperability evaluation system has been applied to the interoperability evaluation of actual UAV systems, and achieved ideal results, proving the feasibility and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0074] Figure 1 A flow chart of a UAV system interoperability evaluation method based on Kalman filtering provided by the present invention.

[0075] Figure 2 This is a schematic diagram of the structure attribute indicator tree structure provided by the present invention.

[0076] Figure 3 A schematic diagram of the application attribute indicator tree structure provided by the present invention.

[0077] Figure 4 This is a schematic diagram of the facility attribute indicator tree structure provided by the present invention.

[0078] Figure 5This is a schematic diagram of the data attribute indicator tree structure provided by the present invention.

[0079] Figure 6 This is a schematic diagram of the knowledge attribute indicator tree structure provided by the present invention.

[0080] Figure 7 This is a schematic diagram of the overall attribute indicator tree structure provided by the present invention.

[0081] Figure 8 A schematic diagram of the overall attribute index tree structure with weighted operators provided by the present invention.

[0082] Fig. 9 A schematic diagram of the structure of an unmanned aerial vehicle system interoperability evaluation system based on Kalman filtering provided by the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] Example 1

[0085] like Figure 1 As shown, the embodiment of the present invention discloses a method for evaluating interoperability of a UAV system based on Kalman filtering, comprising:

[0086] Construct a UAV system interoperability level model including structure attributes, application attributes, facility attributes, data attributes and knowledge attributes;

[0087] Based on the above attributes, construct the secondary index trees respectively and merge them to obtain the overall attribute index tree;

[0088] Scoring is performed based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute;

[0089] Based on the corresponding secondary index scores and weight operators, the corresponding attribute state equation is obtained;

[0090] A first attribute value is obtained by fusing the structural attribute state equation with the application attribute state equation;

[0091] A second attribute value is obtained by fusing the first attribute value and the facility attribute state equation;

[0092] The above fusion process is executed cyclically, and the data attribute state equation and the knowledge attribute state equation are fused in sequence, and finally the fourth attribute value is obtained;

[0093] Based on the fourth attribute value and the UAV system interoperability level model, the interoperability level is obtained.

[0094] Example 2

[0095] The embodiment of the present invention discloses a method for evaluating interoperability of an unmanned aerial vehicle system based on Kalman filtering, comprising:

[0096] Construct a UAV system interoperability level model including structure attributes, application attributes, facility attributes, data attributes and knowledge attributes:

[0097] Preferably, the LISI model provides a reference framework for defining and evaluating system interoperability, but with the deepening of practice, the system interoperability level evaluated by the LISI model is inconsistent with the actual work. Although the term "unmanned" is used for UAVs, a large number of people are still required to achieve the expected interoperability task efficiency. For example, the human members implicit in the ground control station determine the system-level characteristics of a UAV and also play a vital role in system safety. As far as the entire UAV development process is concerned, even if the system engineering approach is used for coordination, its scope is limited by the technology of human members, that is, the UAV system does not remove the human factor from the system. Therefore, when evaluating the interoperability of the UAV system, the human factor has to be considered. The ability of a person is often determined by his knowledge reserve. Therefore, directly considering the knowledge attribute of a person is the basis for a complete interoperability evaluation, thereby increasing the knowledge attribute K.

[0098] Preferably, the UAV system interoperability level model includes:

[0099] The isolation level is 0, the connection level is 1, the functional level is 2, the integration level is 3, the collaboration level is 4, and the general level is 5.

[0100] Preferably, the unmanned system interoperability level model of this embodiment is shown in Table 1:

[0101] Table 1 Unmanned system interoperability level model

[0102]

[0103] Based on the above attributes, construct the secondary index trees respectively and merge them to obtain the overall attribute index tree:

[0104] Preferably, Figure 2-Figure 6 As shown, in order to quantitatively evaluate the interoperability of UAV systems, secondary index trees are constructed based on the above attributes, and the corresponding index trees are the structure attribute index tree, application attribute index tree, facility attribute index tree, data attribute index tree and knowledge attribute index tree.

[0105] The above attribute indicator trees all include: target attributes, multiple first-level indicators and multiple second-level indicators;

[0106] Each first-level indicator corresponds to multiple second-level indicators.

[0107] Preferably, the target attributes include: structure attributes, application attributes, facility attributes, data attributes and knowledge attributes.

[0108] Preferably, the first-level indicators of the structural attribute indicator tree include: data structure P1(k), system structure P2(k) and management structure P3(k);

[0109] The first-level indicators of the application attribute indicator tree include: interaction model A1(k), implementation work A2(k) and application scale A3(k);

[0110] The first-level indicators of the facility attribute indicator tree include: facility controllability I1(k), protocol practicality I2(k), and facility robustness I3(k);

[0111] The first-level indicators of the data attribute indicator tree include: simplex D1(k), duplex and half-duplex D2(k), and duplex D3(k);

[0112] The first-level indicators of the knowledge attribute indicator tree include: transmission information K1(k), understanding information K2(k) and prediction information K3(k).

[0113] Preferably, the secondary indicators of the data structure include: basic data standard P 11 (k-1), data service standard P 12 (k-1) and data management standard P 13 (k-1);

[0114] The secondary indicators of the system structure include: physical layer P 21 (k-1), data layer P 22 (k-1) and semantic layer P 23 (k-1);

[0115] The secondary indicators of management structure include: 31 (k-1), universality P 32 (k-1) and integration P 33 (k-1);

[0116] The secondary indicators of the interaction model include: interaction delay A 11 (k-1), interaction mode A 12 (k-1) and interaction direction A 13 (k-1);

[0117] The secondary indicators for implementation operations include: Durability A21 (k-1), flight altitude A 22 (k-1) and speed A 23 (k-1);

[0118] The secondary indicators of application scale include: System quantity A 31 (k-1), the number of software A 32 (k-1) and the number of hardware A 33 (k-1);

[0119] The secondary indicators of facility controllability include: Command controllability I 11 (k-1), management controllability I 12 (k-1) and collaborative controllability I 13 (k-1);

[0120] The secondary indicators of protocol practicality include: Protocol Architecture I 21 (k-1), support scale I 22 (k-1) and service type I 23 (k-1);

[0121] The secondary indicators of facility robustness include: Link integrity I 31 (k-1), information redundancy I 32 (k-1) and anti-destruction capability I 33 (k-1);

[0122] The secondary indicators of simplex include: communication confidentiality D 11 (k-1), communication timeliness D 12 (k-1) and information capacity D 13 (k-1);

[0123] The secondary indicators of dual half-work include: instruction correctness D 21 (k-1), feedback timeliness D 22 (k-1) and data confidentiality D 23 (k-1);

[0124] The secondary indicators of duplex include: data confidentiality D 31 (k-1), communication fluency D 32 (k-1) and information acquisition degree D 33 (k-1);

[0125] The secondary indicators of the transmitted information include: Image K 11 (k-1), video K 12 (k-1) and speech K 13 (k-1);

[0126] Secondary indicators for understanding information include: Knowledge network K21 (k-1), environmental model K 22 (k-1) and the target estimate K 23 (k-1);

[0127] The secondary indicators of forecast information include: situation forecast K 31 (k-1), model correction K 32 (k-1) and command decision K 33 (k-1).

[0128] Preferably, for the convenience of calculation, the above five attribute index trees are combined and simplified to obtain an overall attribute index tree as follows: Figure 7 As shown, the overall attribute indicator tree is divided into 4 layers:

[0129] The first level is the interoperability level of the drone system: after quantification, the average value of the isolation level is 0, the average value of the connection level is 1, the average value of the function level is 2, the average value of the integration level is 3, the average value of the collaboration level is 4, and the average value of the general level is 5; in actual evaluation, the boundaries between the levels are fuzzy, and the evaluation results may fluctuate above and below the average value;

[0130] The second layer is the target attributes: structure attributes P(k), application attributes A(k), facility attributes I(k), data attributes D(k) and knowledge attributes K(k);

[0131] The third layer is the first-level index: corresponding to the data structure P1(k), system structure P2(k), management structure P3(k), interaction model A1(k), ... in the attribute index tree, where k represents the state at the next moment;

[0132] The fourth layer is the secondary index: the basic data standard P in the index tree is represented in the form of vectors. 11 (k-1), data service standard P 12 (k-1), data management standard P 13 (k-1),..., where k-1 represents the current state.

[0133] Scoring is performed based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute;

[0134] Preferably, the process of evaluating the system interoperability level by the LISI model can be summarized as follows: prepare a questionnaire based on the secondary interoperability indicators; interview technical personnel or domain experts to obtain data to fill in the questionnaire; map the questionnaire to the interoperability level model to form a system interoperability profile; perform attribute analysis on the profile to finally obtain the system interoperability level.

[0135] Preferably, the questionnaire is used as a data source, which directly affects the evaluation results. Human intervention introduces new forms of uncertainty to the data information provided. First, there is a lack of sufficient knowledge of the statistical phenomena implicit in practice, and second, there is inherent ambiguity in the process of constructing operational data. A recognized standard for modeling fuzzy information is formalized Bayesian modeling, which uses a formalized motion model (likelihood function) that describes the transient behavior of the target to correct the formalized observation model (prior probability) that is independent of the implementation, and obtain a posterior probability with a smaller variance, thereby achieving the purpose of reducing noise and increasing uncertainty. However, the calculation of infinite integrals in the formalized Bayesian model usually has no analytical solution. The present invention uses Kalman filtering, a method that is a generalization of the formalized Bayesian model, to evaluate system interoperability, which solves the problem of the lack of analytical solutions for infinite integrals in the formalized Bayesian model.

[0136] Preferably, multiple secondary index scores and multiple weight operators corresponding to each attribute are obtained, specifically including:

[0137] Scoring the secondary indicators of all attributes based on the expert experience of relevant professionals, and obtaining multiple secondary indicator scores corresponding to each attribute;

[0138] Based on the expert experience of relevant professionals, multiple first weight operators are given between the primary index of each attribute and the corresponding attribute, and multiple second weight operators are given between the secondary index of each attribute and the corresponding primary index;

[0139] A weight operator is composed based on the first weight operator and the second weight operator.

[0140] Preferably, each primary indicator of an attribute corresponds to a different first weight operator. X∈{P,A,I,D,K}, i∈{1,2,3}, i.e. the first weight operator The total weight of the attribute is allocated; the secondary indicators under the primary indicator of each attribute correspond to different second weight operators j∈{1,2,3}, the second weight operator The total weight of the corresponding first-level indicators is allocated; the overall attribute indicator tree with weight operator is as follows Figure 8 As shown in the figure, the larger the weight operator is, the greater the weight of the indicator is in the evaluation.

[0141] Preferably, the secondary indicator score corresponding to the secondary indicator with the largest second weight operator among the primary indicators of each attribute participates in the calculation of the attribute state equation.

[0142] Based on the corresponding secondary index score and weight operator, the corresponding attribute state equation is obtained:

[0143] Preferably, the corresponding property state equation is obtained, specifically including:

[0144] Obtaining a corresponding attribute state matrix based on a first weight operator and a second weight operator corresponding to each attribute;

[0145] Based on the attribute state matrix and the corresponding secondary index score, the corresponding attribute state quantity prediction equation X(k) is calculated:

[0146]

[0147] Among them, k represents the state at the next moment, F X represents the X attribute state matrix, represents the vector composed of all secondary indicator scores of attribute X, k-1 represents the current state, P, A, I, D and K represent structural attributes, application attributes, facility attributes, data attributes and knowledge attributes respectively;

[0148] Based on the attribute state quantity prediction equation X(k), the corresponding attribute covariance prediction equation is obtained:

[0149]

[0150] Among them, ∑ X(k-1) represents the covariance matrix of the X attribute at the previous moment, and T represents the transposed symbol;

[0151] The attribute state quantity prediction equation and the attribute covariance prediction equation together constitute the attribute state equation.

[0152] Preferably, taking structural attributes as an example, the structural attribute state quantity prediction equation P(k) is:

[0153]

[0154]

[0155] Among them, P 11 (k-1) represents the score of the basic data standard. Similarly, other P ij (k-1) has the same meaning as above. i∈{1,2,3} represents different first-level weight operators corresponding to the first-level indicators of structural attributes. j∈{1,2,3} represents different secondary weight operators corresponding to the secondary indicators of structural attributes.

[0156] Will is the structural attribute state matrix F P , the above formula is simplified to:

[0157]

[0158] in, Represents the vector composed of all secondary indicator scores of structural attributes;

[0159] Similarly, the prediction equation of the applied attribute state quantity can be obtained:

[0160]

[0161] Among them, F A represents the application attribute state matrix, Represents the vector composed of all secondary indicator scores of application attributes;

[0162] Facility attribute state quantity prediction equation:

[0163]

[0164] Among them, F I represents the facility attribute state matrix, Represents the vector composed of all secondary indicator scores of facility attributes;

[0165] Data attribute state quantity prediction equation:

[0166]

[0167] Among them, F D represents the data attribute state matrix, Represents the vector composed of all secondary indicator scores of the data attribute;

[0168] Knowledge attribute state quantity prediction equation:

[0169]

[0170] Among them, F K represents the knowledge attribute state matrix, Represents a vector consisting of all secondary indicator scores of knowledge attributes.

[0171] Preferably, taking structural attributes as an example, the structural attribute covariance prediction equation is:

[0172]

[0173]

[0174] Among them, ∑ P(k-1) Represents the covariance matrix of the structural attribute at the previous moment, and T represents the transposed sign.

[0175] Preferably, the initial values ​​of any two different secondary indicators in the covariance matrix are given by technical personnel, wherein the covariance is greater than 0 when the initial values ​​of any two different secondary indicators tend to be consistent, and the covariance is equal to 0 when they are independent of each other. Similarly, the covariance matrix of attributes A, I, D, and K can be obtained.

[0176] Similarly, the attribute covariance prediction equation can be obtained:

[0177]

[0178] Among them, ∑ A(k-1) Represents the covariance matrix of the application attribute at the previous moment;

[0179] Facility attribute covariance prediction equation:

[0180]

[0181] Among them, ∑ I(k-1) represents the covariance matrix of facility attributes at the previous moment;

[0182] Data attribute covariance prediction equation:

[0183]

[0184] Among them, ∑ D(k-1) Represents the covariance matrix of the data attribute at the previous moment;

[0185] Knowledge attribute covariance prediction equation:

[0186]

[0187] Among them, ∑ K(k-1) Represents the covariance matrix of the knowledge attribute at the previous moment.

[0188] Preferably, the structural property state equation is:

[0189]

[0190]

[0191] The applied property state equation is:

[0192]

[0193] The facility attribute state equation is:

[0194]

[0195]

[0196] The data attribute state equation is:

[0197]

[0198] The knowledge attribute state equation is:

[0199]

[0200] Based on the fusion of the structural attribute state equation and the application attribute state equation, the first attribute value is obtained:

[0201] Preferably, in the prediction equation of the state quantity, the state at the current moment (k-1) is used to predict the state quantity at the next moment k, and in the prediction equation of the covariance, the uncertainty at the moment k is predicted. In addition to the data error caused by the expert evaluation, the uncertainty of the model, the uncertainty of the parameters, etc. will cause the deviation of the results, and these deviations are all processed by the Kalman filter. In addition, the evaluation system is a static system without external forces, and no control variables are set here. There is no real value added to the prediction equation, and even if the prediction is continued, the result may not be accurate. Taking the next attribute as the real observed value and updating the prediction equation, a more accurate estimate can be obtained.

[0202] Preferably, the fusion process is:

[0203] Calculate the Kalman gain M based on the property state equation k :

[0204]

[0205] Among them, F Y represents the Y attribute state matrix, and R represents the comprehensive noise;

[0206] Calculate the nth attribute value P(k+n) based on the Kalman gain:

[0207] P(k+n)=P(k)+M k (Y(1)-F Y P(k)

[0208] Based on the nth attribute value, the attribute value covariance matrix ∑ P(k+n) :

[0209] ∑ P(k+n) =(1-M k F Y )∑ P(k)

[0210] Where n represents the number of fusions.

[0211] Preferably, the structural property state equation is: As the prediction equation, the property state equation will be applied as: As the observation equation; based on the first fusion of the structural attribute state equation and the application attribute state equation, that is, when k = 1, the first attribute value P(2) is obtained:

[0212] When k = 1, by giving ∑ P(0), based on the secondary index score of the structural attribute and the first and second weight operators of the structural attribute, we can obtain P(1);

[0213] When k = 1, the secondary index scores of the application attributes and the first and second weight operators of the application attributes are solved to obtain A(1) and F A ;

[0214] The Kalman gain M1 is calculated based on the property state equation:

[0215]

[0216] At this time, the comprehensive noise R is 0;

[0217] The first attribute value P(2) is calculated based on the Kalman gain M1 and P(1):

[0218] P(2)=P(1)+M1(A(1)-F A P(1)

[0219] Based on the first attribute value, the attribute value covariance matrix ∑ P(2) :

[0220] ∑ P(2) =(1-M1F A )∑ P(1) .

[0221] Based on the fusion of the first attribute value and the facility attribute state equation, the second attribute value is obtained:

[0222] Preferably, a second fusion is performed based on the structural attribute state equation and the facility attribute state equation, that is, when k=2, to obtain the second attribute value P(3):

[0223] When k = 1, the secondary index scores of the facility attributes and the first and second weight operators of the facility attributes are solved to obtain I(1) and F I ;

[0224] The Kalman gain M2 is calculated based on the property state equation:

[0225]

[0226] The second attribute value P(3) is calculated based on the Kalman gain M2 and P(2):

[0227] P(3)=P(2)+M2(I(1)-F I P(2)

[0228] Based on the second attribute value, the attribute value covariance matrix ∑ P(3) :

[0229] ∑ P(3)=(1-M2F I )∑ P(2) .

[0230] The above fusion process is executed cyclically, and the data attribute state equation and the knowledge attribute state equation are fused in turn, and finally the fourth attribute value is obtained:

[0231] Preferably, the third attribute value P(4) is obtained by performing a third fusion based on the structural attribute state equation and the data attribute state equation, that is, when k=3:

[0232] When k = 1, D(1) and F are obtained based on the secondary index score of the data attribute and the first and second weight operators of the data attribute. D ;

[0233] The Kalman gain M3 is calculated based on the property state equation:

[0234]

[0235] The third attribute value P(4) is calculated based on the Kalman gain M2 and P(3):

[0236] P(4)=P(3)+M3(D(1)-F D P(3)

[0237] Based on the third attribute value, the attribute value covariance matrix ∑ P(4) :

[0238] ∑ P(4) =(1-M3F D )∑ P(3) ;

[0239] Based on the fourth fusion of the structural attribute state equation and the knowledge attribute state equation, that is, when k=4, the fourth attribute value P(5) is obtained:

[0240] When k = 1, K(1) and F are obtained by solving the secondary index score of the knowledge attribute and the first and second weight operators of the knowledge attribute. K

[0241] The Kalman gain M4 is calculated based on the property state equation:

[0242]

[0243] The fourth attribute value P(5) is calculated based on the Kalman gain M4 and P(4):

[0244] P(5)=P(4)+M4(K(1)-F K P(4)

[0245] Based on the second attribute value, the attribute value covariance matrix Σ is obtained P(5):

[0246] ∑ P(5) =(1-M4F K )Σ P(4) .

[0247] Based on the fourth attribute value and the UAV system interoperability level model, the interoperability level is obtained.

[0248] Preferably, a search is performed in the UAV system interoperability level model based on the fourth attribute value P(5) to obtain the final interoperability level.

[0249] Example 3

[0250] According to expert experience, the secondary indicators of the evaluated UAV system attribute indicator tree are scored, and the corresponding primary weight operators and secondary weight operators are given, as shown in Table 2:

[0251] Table 2 Secondary indicator scoring table

[0252]

[0253]

[0254] As can be seen from Table 1, the interoperability level is quantified from 0 to 5, and the integer represents the mean of the level. There is no clear boundary between the levels, so the decimals in the scoring results represent uncertainty.

[0255] Determine the state equation and observation equation

[0256] Take the second-level index with the largest weight as the first-level index of structural attributes and substitute it into the above formula to get:

[0257]

[0258] Calculate the Kalman gain:

[0259]

[0260] Fusion Update:

[0261]

[0262] Similarly, we can get:

[0263]

[0264] Substitute them into the calculation of Kalman gain and get the corresponding results:

[0265]

[0266] and

[0267] The second attribute value P(3) is calculated based on the Kalman gain M2 and P(2):

[0268] The third attribute value P(4) is calculated based on the Kalman gain M3 and P(3):

[0269] The fourth attribute value P(5) is calculated based on the Kalman gain M4 and P(4):

[0270] The final P(5) vector consists of three elements, which have been weighted. The final evaluation result is the average: (4.35+3.12+3.22) / 3=3.5633, that is, the fourth attribute value P(5)=3.5633. Based on the unmanned system interoperability level model in Table 1, it can be seen that 3.5633 is between 3 (integration level) and 4 (collaboration level), which is close to 4, so it is judged to be the collaboration level.

[0271] The present invention first expands the LISI model, adds knowledge attributes, and improves the accuracy of UAV interoperability evaluation. Secondly, the interoperability evaluation is performed on five attributes by introducing the classic algorithm Kalman filter, and an attribute index tree is proposed. Finally, an example is given, which achieves the purpose of making the estimated value more accurate by fusing the secondary index scores of the five attributes P, A, I, D, and K of the UAV system. At present, the UAV system interoperability evaluation device developed using this technology has been actually promoted and applied.

[0272] Example 4

[0273] like Fig. 9 As shown, a UAV system interoperability evaluation system based on Kalman filtering includes: a hierarchical model building module, an indicator tree building module, a scoring module, a state equation building module, a loop fusion module and a result output module;

[0274] A hierarchical model building module, used to build a hierarchical model of UAV system interoperability including structural attributes, application attributes, facility attributes, data attributes and knowledge attributes;

[0275] An indicator tree construction module is used to construct secondary indicator trees based on the above attributes and merge them to obtain an overall attribute indicator tree;

[0276] The scoring module is used to score based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute;

[0277] A state equation building module is used to obtain the corresponding attribute state equation based on the corresponding secondary indicator score and weight operator;

[0278] A loop fusion module is used to fuse the structure attribute state equation with the application attribute state equation to obtain a first attribute value; fuse the first attribute value with the facility attribute state equation to obtain a second attribute value; and loop through the above fusion process to fuse the data attribute state equation and the knowledge attribute state equation in turn to finally obtain a fourth attribute value;

[0279] The result output module is used to obtain the interoperability level based on the fourth attribute value and the UAV system interoperability level model.

[0280] Example 5

[0281] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0282] Memory, used to store computer programs;

[0283] The processor, when used to execute the program stored in the memory, can implement a Kalman filtering-based UAV system interoperability assessment method as in Example 1 or 2.

[0284] The electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The processor may call the logic instructions in the memory to execute a Kalman filter-based UAV system interoperability assessment method in Embodiment 1 or 2.

[0285] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0286] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating interoperability of unmanned aerial vehicle systems based on Kalman filtering, characterized in that: include: Construct a UAV system interoperability level model including structure attributes, application attributes, facility attributes, data attributes and knowledge attributes; Based on the above attributes, construct the secondary index trees respectively and merge them to obtain the overall attribute index tree; Scoring is performed based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute; Obtaining a corresponding attribute state equation based on the corresponding secondary index score and the weight operator; A first attribute value is obtained by fusing the structural attribute state equation with the application attribute state equation; Based on the fusion of the first attribute value and the facility attribute state equation, a second attribute value is obtained; The above fusion process is executed cyclically, and the data attribute state equation and the knowledge attribute state equation are fused in sequence, and finally the fourth attribute value is obtained; Based on the fourth attribute value and the UAV system interoperability level model, an interoperability level is obtained.

2. The UAV system interoperability evaluation method based on Kalman filtering according to claim 1 is characterized in that: The UAV system interoperability level model includes: The isolation level is 0, the connection level is 1, the functional level is 2, the integration level is 3, the collaboration level is 4, and the general level is 5.

3. The UAV system interoperability evaluation method based on Kalman filtering according to claim 1 is characterized in that: Based on the above attributes, secondary index trees are constructed respectively, and the corresponding index trees are structure attribute index tree, application attribute index tree, facility attribute index tree, data attribute index tree and knowledge attribute index tree; The above attribute indicator trees all include: target attributes, multiple first-level indicators and multiple second-level indicators; Each of the first-level indicators corresponds to a plurality of the second-level indicators.

4. The method for evaluating interoperability of unmanned aerial vehicle systems based on Kalman filtering according to claim 3 is characterized in that: The first-level indicators of the structural attribute indicator tree include: data structure, system structure and management structure; The first-level indicators of the application attribute indicator tree include: interaction model, implementation operation and application scale; The first-level indicators of the facility attribute indicator tree include: facility controllability, protocol practicality and facility robustness; The first-level indicators of the data attribute indicator tree include: simplex, duplex and half-duplex, and duplex; The first-level indicators of the knowledge attribute indicator tree include: transmission information, understanding information and prediction information.

5. The method for evaluating interoperability of unmanned aerial vehicle systems based on Kalman filtering according to claim 4 is characterized in that: The secondary indicators of the data structure include: basic data standards, data service standards and data management standards; The secondary indicators of the system structure include: physical layer, data layer and semantic layer; The secondary indicators of the management structure include: standardization, universality and integration of regulations and rules; The secondary indicators of the interaction model include: interaction delay, interaction mode and interaction direction; The secondary indicators for implementing the operation include: endurance, flight altitude and speed; The secondary indicators of the application scale include: the number of systems, the number of software and the number of hardware; The secondary indicators of facility controllability include: command controllability, management controllability and collaboration controllability; The secondary indicators of the protocol practicality include: protocol architecture, support scale and service type; The secondary indicators of facility robustness include: link integrity, information redundancy and anti-destruction capability; The secondary indicators of the simplex include: communication confidentiality, communication timeliness and information capacity; The secondary indicators of the dual half-work include: instruction correctness, feedback timeliness and data confidentiality; The secondary indicators of duplex include: data confidentiality, communication fluency, and information acquisition; The secondary indicators of the transmission information include: images, videos and voices; The secondary indicators of understanding information include: knowledge network, environment model and target estimation; The secondary indicators of the forecast information include: situation forecast, model correction and command decision.

6. The method for evaluating interoperability of unmanned aerial vehicle systems based on Kalman filtering according to claim 5 is characterized in that: Get multiple secondary index scores and multiple weight operators corresponding to each attribute, including: Scoring the secondary indicators of all attributes based on the expert experience of relevant professionals, and obtaining multiple secondary indicator scores corresponding to each attribute; Based on the expert experience of the relevant professionals, a plurality of first weight operators are provided between the primary index of each attribute and the corresponding attribute, and a plurality of second weight operators are provided between the secondary index of each attribute and the corresponding primary index; The weight operator is composed based on the first weight operator and the second weight operator.

7. The UAV system interoperability evaluation method based on Kalman filtering according to claim 6 is characterized in that: The corresponding property state equation is obtained, including: Obtaining a corresponding attribute state matrix based on the first weight operator and the second weight operator corresponding to each attribute; Based on the attribute state matrix and the corresponding secondary index score, the corresponding attribute state quantity prediction equation X(k) is calculated: Among them, k represents the state at the next moment, F X represents the X attribute state matrix, represents the vector composed of all secondary indicator scores of attribute X, k-1 represents the current state, P, A, I, D and K represent structural attributes, application attributes, facility attributes, data attributes and knowledge attributes respectively; Based on the attribute state quantity prediction equation X(k), the corresponding attribute covariance prediction equation is obtained: Among them, ∑ X(k-1) represents the covariance matrix of the X attribute at the previous moment, and T represents the transposed symbol; The attribute state quantity prediction equation and the attribute covariance prediction equation together constitute the attribute state equation.

8. The method for evaluating interoperability of unmanned aerial vehicle systems based on Kalman filtering according to claim 7, characterized in that: The secondary indicator score corresponding to the secondary indicator with the largest second weight operator among the primary indicators of each attribute participates in the calculation of the attribute state equation.

9. The UAV system interoperability evaluation method based on Kalman filtering according to claim 7 is characterized in that: The fusion process is: The Kalman gain M is calculated based on the property state equation k : Among them, F Y represents the Y attribute state matrix, and R represents the comprehensive noise; The nth attribute value P(k+n) is calculated based on the Kalman gain: P(k+n)=P(k)+M k (Y(1)-F Y P(k)) Based on the nth attribute value, the attribute value covariance matrix ∑ P(k+n) : ∑ P(k+n) =(1-M k F Y )∑ P(k) Where n represents the number of fusions.

10. A UAV system interoperability evaluation system based on Kalman filtering, applied to any evaluation method as described in claims 1-9, characterized in that: include: Hierarchical model building module, indicator tree building module, scoring module, state equation building module, loop fusion module and result output module; The hierarchical model building module is used to build a UAV system interoperability hierarchical model including structural attributes, application attributes, facility attributes, data attributes and knowledge attributes; The indicator tree construction module is used to construct secondary indicator trees based on the above attributes and merge them to obtain an overall attribute indicator tree; The scoring module is used to score based on the overall attribute indicator tree to obtain multiple secondary indicator scores and multiple weight operators corresponding to each attribute; The state equation construction module is used to obtain the corresponding attribute state equation based on the corresponding secondary index score and the weight operator; The loop fusion module is used to fuse the structure attribute state equation with the application attribute state equation to obtain a first attribute value; fuse the first attribute value with the facility attribute state equation to obtain a second attribute value; and loop through the above fusion process to fuse the data attribute state equation and the knowledge attribute state equation in turn to finally obtain a fourth attribute value; The result output module is used to obtain the interoperability level based on the fourth attribute value and the UAV system interoperability level model.

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